Job Description

Position Overview

We are seeking a highly skilled Part-Time Bioinformatics Scientist to support RNA-seq data analysis and machine learning model development for biomarker discovery and clinical diagnostics. This is a flexible role ideal for individuals seeking to contribute to cutting-edge translational genomics projects on a part-time basis.

Key Responsibilities

• Analyze RNA-seq datasets, including preprocessing, QC, normalization, and differential expression analysis
• Develop and maintain bioinformatics pipelines for transcriptomic data
• Apply statistical and machine learning methods to identify diagnostic or predictive biomarkers
• Build and evaluate predictive models, including deep learning approaches
• Explore applications of large language models (LLMs) or multimodal AI in biological data analysis
• Collaborate with cross-functional teams to interpret results and guide experimental design
• Communicate findings through reports, visualizations, and presentations

Qualifications

Basic Qualifications

• PhD candidate or PhD degree in Bioinformatics, Computational Biology, Biology, or a related field
• Hands-on experience with RNA-seq data analysis
• Strong programming skills in R and/or Python
• Familiarity with Linux/Unix environments and proficiency in bash scripting
• Formal training in statistics and machine learning

Preferred Qualifications

• Experience with machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
• Experience developing or applying deep learning models
• Familiarity with large language models (LLMs) or generative AI applications in biology
• Experience with NGS pipelines and tools (e.g., STAR, Salmon, nf-core, etc.)
• Experience working in high-performance computing (HPC) or cloud environments
• Strong data visualization and communication skills

Additional Information

What You’ll Gain

• Opportunity to work on cutting-edge RNA-seq datasets from clinical samples
• Exposure to real-world biomarker discovery and diagnostic development
• Flexible, part-time engagement with meaningful scientific impact
• Collaboration with a highly experienced, multidisciplinary team